December 22, 2016

In this paper we evaluate strategies to reduce the required processing capacity in a Cloud-Radio Access Network (C-RAN) architecture by improving the placement of user processing tasks. Our approach of assigning compute tasks in a pool of compute resources is based on fine granular tasks, where one compute task per served user is introduced. We compare different strategies in order to balance the load in the pool and save processing resources. Therefore we evaluate the best possible reallocation method by formulating an optimization problem including extensions to reduce the number of reassignments. We also introduce an algorithm for dynamic reallocations that can be implemented in real systems. From the evaluation results we can conclude that all strategies reduce the total overload by enhanced load balancing. Further all strategies improve the perceived Quality of Experience (QoE) of individual users.

Recent Publications

August 09, 2017

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...

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